Semiparametric transformation models with random effects for joint analysis of recurrent and terminal events.
We propose a broad class of semiparametric transformation models with random effects for the joint analysis of recurrent events and a terminal event. The transformation models include proportional hazards/intensity and proportional odds models. We estimate the model parameters by the nonparametric maximum likelihood approach. The estimators are shown to be consistent, asymptotically normal, and asymptotically efficient. Simple and stable numerical algorithms are provided to calculate the parameter estimators and to estimate their variances. Extensive simulation studies demonstrate that the proposed inference procedures perform well in realistic settings. Applications to two HIV/AIDS studies are presented.
Zeng
Donglin
D
Department of Biostatistics, CB 7420, University of North Carolina, Chapel Hill, North Carolina 27599-7420, USA. dzeng@bios.unc.edu
Lin
D Y
DY
D001699:Biometry / Q000379:methods*; D002986:Clinical Trials as Topic; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D015987:Effect Modifier, Epidemiologic; D023381:Endpoint Determination / Q000379:methods*; D006801:Humans; D008137:Longitudinal Studies; D015233:Models, Statistical; D016016:Proportional Hazards Models; D012008:Recurrence; D012044:Regression Analysis